The metric of a usecase is the function that will be used to assess for the efficiency of its models. The metrics you can choose depends on the type of usecase you are training.

class previsionio.metrics.Classification

Metrics for classification projects Available metrics in prevision:

auc, log_loss, error_rate_binary
AUC = 'auc'

Area Under ROC Curve

error_rate = 'error_rate_binary'

Error rate

log_loss = 'log_loss'

Logarithmic Loss

class previsionio.metrics.Clustering

Metrics for clustering projects

calinski_harabaz = 'calinski_harabaz'

Clustering calinski_harabaz metric

silhouette = 'silhouette'

Clustering silhouette metric

class previsionio.metrics.MultiClassification

Metrics for multiclassification projects

error_rate = 'error_rate_multi'

Multi-class Error rate

log_loss = 'log_loss'

Logarithmic Loss

class previsionio.metrics.Regression

Metrics for regression projects Available metrics in prevision:

rmse, mape, rmsle, mse, mae
MAE = 'mae'

Mean Average Error

MAPE = 'mape'

Mean Average Percentage Error

MSE = 'mse'

Mean squared Error

RMSE = 'rmse'

Root Mean Squared Error

RMSLE = 'rmsle'

Root Mean Squared Logarithmic Error

class previsionio.metrics.TextSimilarity

Metrics for text similarity projects

accuracy_at_k = 'accuracy_at_k'

Accuracy At K

mrr_at_k = 'mrr_at_k'